Machine Learning Engineer

Role Overview: As a Machine Learning Engineer, you will play a central role in translating cutting-edge machine learning research into scalable, production-ready solutions. You will collaborate closely with cross-functional teams to identify opportunities where ML can drive product value, architect robust model-centric systems, and ensure their seamless integration into real-world applications. The role requires a strong balance between theoretical understanding and engineering execution, with a focus on building reliable, maintainable, and high-impact AI-driven features that align with Nace.AI http://Nace.AI’s strategic objectives. Key Responsibilities: - Design, build, and maintain end-to-end ML systems, including synthetic data pipelines, model training, debugging, and performance evaluation. - Fine-tune large language models (LLMs) and implement meta-learning methods to enhance model generalization and efficiency. - Improve existing Nace.AI http://Nace.AI models by incorporating advancements from recent ML research. Qualifications: - Hands-on experience training and fine-tuning large language models (LLMs) and vision-language models (VLMs), including practical work with pre-training, instruction tuning, and alignment techniques (GRPO,RLHF/DPO/PPO). - Hands-on Experience with Deep Learning Models, especially Transformers. - Ability to translate cutting-edge research from papers into clean, production-ready code (Paper to Code). - Proven experience scaling inference infrastructure for LLMs/VLMs, including expertise in model serving frameworks like vLLM, TGI. - Proficient in Python with a strong track record of building substantial projects. - Solid foundation in computer science fundamentals (data structures, algorithms, design patterns). - BS degree in CS or related technical field. - Solid Experience with ML frameworks and libraries (PyTorch, TensorFlow). - Self-starter comfortable working in a fast-paced, dynamic environment. Preferred Qualifications: - MS/PhD in CS or related technical field. - Familiarity with data processing stacks such as Spark and Airflow. - Experience with multi-node GPU training. - Contributor to open-source ML projects. - Deep knowledge in Linear Programming. - Experience with advanced NLP and Multimodal post-training experience (e.g., model distillation, quantization, deployment optimization). - Experienced in inference time optimization, deep understanding of LLM serving optimizations for LLMs/VLMs. - Hands on experience with quantization techniques (AWQ, GPTQ, FP8/GGUF).